import torch.utils.data as data
from PIL import Image
import os
import os.path
[docs]class CocoCaptions(data.Dataset):
"""`MS Coco Captions <http://mscoco.org/dataset/#captions-challenge2015>`_ Dataset.
Args:
root (string): Root directory where images are downloaded to.
annFile (string): Path to json annotation file.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.ToTensor``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
Example:
.. code:: python
import torchvision.datasets as dset
import torchvision.transforms as transforms
cap = dset.CocoCaptions(root = 'dir where images are',
annFile = 'json annotation file',
transform=transforms.ToTensor())
print('Number of samples: ', len(cap))
img, target = cap[3] # load 4th sample
print("Image Size: ", img.size())
print(target)
Output: ::
Number of samples: 82783
Image Size: (3L, 427L, 640L)
[u'A plane emitting smoke stream flying over a mountain.',
u'A plane darts across a bright blue sky behind a mountain covered in snow',
u'A plane leaves a contrail above the snowy mountain top.',
u'A mountain that has a plane flying overheard in the distance.',
u'A mountain view with a plume of smoke in the background']
"""
def __init__(self, root, annFile, transform=None, target_transform=None):
from pycocotools.coco import COCO
self.root = os.path.expanduser(root)
self.coco = COCO(annFile)
self.ids = list(self.coco.imgs.keys())
self.transform = transform
self.target_transform = target_transform
[docs] def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: Tuple (image, target). target is a list of captions for the image.
"""
coco = self.coco
img_id = self.ids[index]
ann_ids = coco.getAnnIds(imgIds=img_id)
anns = coco.loadAnns(ann_ids)
target = [ann['caption'] for ann in anns]
path = coco.loadImgs(img_id)[0]['file_name']
img = Image.open(os.path.join(self.root, path)).convert('RGB')
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def __len__(self):
return len(self.ids)
[docs]class CocoDetection(data.Dataset):
"""`MS Coco Detection <http://mscoco.org/dataset/#detections-challenge2016>`_ Dataset.
Args:
root (string): Root directory where images are downloaded to.
annFile (string): Path to json annotation file.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.ToTensor``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
"""
def __init__(self, root, annFile, transform=None, target_transform=None):
from pycocotools.coco import COCO
self.root = root
self.coco = COCO(annFile)
self.ids = list(self.coco.imgs.keys())
self.transform = transform
self.target_transform = target_transform
[docs] def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``.
"""
coco = self.coco
img_id = self.ids[index]
ann_ids = coco.getAnnIds(imgIds=img_id)
target = coco.loadAnns(ann_ids)
path = coco.loadImgs(img_id)[0]['file_name']
img = Image.open(os.path.join(self.root, path)).convert('RGB')
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def __len__(self):
return len(self.ids)
def __repr__(self):
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
tmp = ' Target Transforms (if any): '
fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str